Overview

Dataset statistics

Number of variables79
Number of observations258
Missing cells9246
Missing cells (%)45.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory169.4 KiB
Average record size in memory672.2 B

Variable types

Categorical36
Numeric23
Unsupported20

Alerts

ORIGEM has constant value "1"Constant
TIPOBITO has constant value "2"Constant
CODMUNRES has constant value "240810"Constant
TPMORTEOCO has constant value "8.0"Constant
OBITOGRAV has constant value "2.0"Constant
OBITOPUERP has constant value "3.0"Constant
EXAME has constant value "2.0"Constant
CIRURGIA has constant value "2.0"Constant
CAUSABAS has constant value "B342"Constant
COMUNSVOIM has constant value "240810.0"Constant
STCODIFICA has constant value "S"Constant
CODIFICADO has constant value "S"Constant
VERSAOSIST has constant value "3.2.30"Constant
VERSAOSCB has constant value "3.4"Constant
STDOEPIDEM has constant value "0.0"Constant
TPOBITOCOR has constant value "9.0"Constant
TPNIVELINV has constant value "M"Constant
LINHAII has a high cardinality: 108 distinct valuesHigh cardinality
ATESTADO has a high cardinality: 237 distinct valuesHigh cardinality
LOCOCOR is highly imbalanced (51.1%)Imbalance
CODMUNOCOR is highly imbalanced (95.4%)Imbalance
ASSISTMED is highly imbalanced (80.3%)Imbalance
NECROPSIA is highly imbalanced (82.3%)Imbalance
FONTEINV is highly imbalanced (74.9%)Imbalance
STDONOVA is highly imbalanced (96.3%)Imbalance
CAUSABAS_O is highly imbalanced (93.1%)Imbalance
NATURAL has 4 (1.6%) missing valuesMissing
CODMUNNATU has 4 (1.6%) missing valuesMissing
RACACOR has 13 (5.0%) missing valuesMissing
ESTCIV has 11 (4.3%) missing valuesMissing
ESC has 12 (4.7%) missing valuesMissing
ESC2010 has 13 (5.0%) missing valuesMissing
SERIESCFAL has 254 (98.4%) missing valuesMissing
OCUP has 46 (17.8%) missing valuesMissing
CODESTAB has 8 (3.1%) missing valuesMissing
IDADEMAE has 258 (100.0%) missing valuesMissing
ESCMAE has 258 (100.0%) missing valuesMissing
ESCMAE2010 has 258 (100.0%) missing valuesMissing
SERIESCMAE has 258 (100.0%) missing valuesMissing
OCUPMAE has 258 (100.0%) missing valuesMissing
QTDFILVIVO has 258 (100.0%) missing valuesMissing
QTDFILMORT has 258 (100.0%) missing valuesMissing
GRAVIDEZ has 258 (100.0%) missing valuesMissing
SEMAGESTAC has 258 (100.0%) missing valuesMissing
GESTACAO has 258 (100.0%) missing valuesMissing
PARTO has 258 (100.0%) missing valuesMissing
OBITOPARTO has 258 (100.0%) missing valuesMissing
PESO has 258 (100.0%) missing valuesMissing
TPMORTEOCO has 247 (95.7%) missing valuesMissing
OBITOGRAV has 247 (95.7%) missing valuesMissing
OBITOPUERP has 247 (95.7%) missing valuesMissing
ASSISTMED has 135 (52.3%) missing valuesMissing
EXAME has 257 (99.6%) missing valuesMissing
CIRURGIA has 257 (99.6%) missing valuesMissing
NECROPSIA has 138 (53.5%) missing valuesMissing
LINHAA has 23 (8.9%) missing valuesMissing
LINHAB has 32 (12.4%) missing valuesMissing
LINHAC has 95 (36.8%) missing valuesMissing
LINHAD has 189 (73.3%) missing valuesMissing
LINHAII has 92 (35.7%) missing valuesMissing
CB_PRE has 258 (100.0%) missing valuesMissing
COMUNSVOIM has 250 (96.9%) missing valuesMissing
CIRCOBITO has 258 (100.0%) missing valuesMissing
ACIDTRAB has 258 (100.0%) missing valuesMissing
FONTE has 258 (100.0%) missing valuesMissing
DTINVESTIG has 108 (41.9%) missing valuesMissing
ATESTANTE has 26 (10.1%) missing valuesMissing
FONTEINV has 100 (38.8%) missing valuesMissing
CAUSAMAT has 258 (100.0%) missing valuesMissing
ESCMAEAGR1 has 258 (100.0%) missing valuesMissing
ESCFALAGR1 has 13 (5.0%) missing valuesMissing
NUDIASOBCO has 251 (97.3%) missing valuesMissing
DTCADINV has 251 (97.3%) missing valuesMissing
TPOBITOCOR has 251 (97.3%) missing valuesMissing
DTCONINV has 251 (97.3%) missing valuesMissing
TPRESGINFO has 258 (100.0%) missing valuesMissing
TPNIVELINV has 251 (97.3%) missing valuesMissing
TPPOS has 9 (3.5%) missing valuesMissing
ATESTADO is uniformly distributedUniform
DTNASC has unique valuesUnique
IDADEMAE is an unsupported type, check if it needs cleaning or further analysisUnsupported
ESCMAE is an unsupported type, check if it needs cleaning or further analysisUnsupported
ESCMAE2010 is an unsupported type, check if it needs cleaning or further analysisUnsupported
SERIESCMAE is an unsupported type, check if it needs cleaning or further analysisUnsupported
OCUPMAE is an unsupported type, check if it needs cleaning or further analysisUnsupported
QTDFILVIVO is an unsupported type, check if it needs cleaning or further analysisUnsupported
QTDFILMORT is an unsupported type, check if it needs cleaning or further analysisUnsupported
GRAVIDEZ is an unsupported type, check if it needs cleaning or further analysisUnsupported
SEMAGESTAC is an unsupported type, check if it needs cleaning or further analysisUnsupported
GESTACAO is an unsupported type, check if it needs cleaning or further analysisUnsupported
PARTO is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBITOPARTO is an unsupported type, check if it needs cleaning or further analysisUnsupported
PESO is an unsupported type, check if it needs cleaning or further analysisUnsupported
CB_PRE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CIRCOBITO is an unsupported type, check if it needs cleaning or further analysisUnsupported
ACIDTRAB is an unsupported type, check if it needs cleaning or further analysisUnsupported
FONTE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CAUSAMAT is an unsupported type, check if it needs cleaning or further analysisUnsupported
ESCMAEAGR1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
TPRESGINFO is an unsupported type, check if it needs cleaning or further analysisUnsupported
ESC2010 has 45 (17.4%) zerosZeros
ESCFALAGR1 has 45 (17.4%) zerosZeros

Reproduction

Analysis started2023-05-01 23:46:53.574377
Analysis finished2023-05-01 23:48:40.110420
Duration1 minute and 46.54 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ORIGEM
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
258 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 258
100.0%

Length

2023-05-01T23:48:40.240881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:40.437024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 258
100.0%

Most occurring characters

ValueCountFrequency (%)
1 258
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 258
100.0%

TIPOBITO
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
258 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 258
100.0%

Length

2023-05-01T23:48:40.597620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:40.752605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 258
100.0%

Most occurring characters

ValueCountFrequency (%)
2 258
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 258
100.0%

DTOBITO
Real number (ℝ)

Distinct110
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15489038
Minimum1012022
Maximum31072022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:40.890831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1012022
5-th percentile1030522
Q16022022
median15022022
Q324774522
95-th percentile30012022
Maximum31072022
Range30060000
Interquartile range (IQR)18752500

Descriptive statistics

Standard deviation9571005.8
Coefficient of variation (CV)0.61792128
Kurtosis-1.3872735
Mean15489038
Median Absolute Deviation (MAD)9000000
Skewness0.031928668
Sum3.9961717 × 109
Variance9.1604153 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:41.084247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1022022 10
 
3.9%
4022022 9
 
3.5%
6022022 8
 
3.1%
25012022 7
 
2.7%
31012022 6
 
2.3%
26012022 6
 
2.3%
27012022 6
 
2.3%
12022022 6
 
2.3%
4072022 5
 
1.9%
15022022 5
 
1.9%
Other values (100) 190
73.6%
ValueCountFrequency (%)
1012022 3
 
1.2%
1022022 10
3.9%
1032022 1
 
0.4%
1072022 1
 
0.4%
2012022 1
 
0.4%
2022022 4
 
1.6%
2032022 1
 
0.4%
2092022 1
 
0.4%
3012022 2
 
0.8%
3022022 5
1.9%
ValueCountFrequency (%)
31072022 1
 
0.4%
31052022 1
 
0.4%
31012022 6
2.3%
30072022 1
 
0.4%
30052022 1
 
0.4%
30032022 1
 
0.4%
30012022 4
1.6%
29072022 1
 
0.4%
29062022 2
 
0.8%
29012022 3
1.2%

HORAOBITO
Real number (ℝ)

Distinct216
Distinct (%)84.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1137.8327
Minimum0
Maximum2350
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:41.280116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104.2
Q1600
median1100
Q31703
95-th percentile2300.6
Maximum2350
Range2350
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation679.25037
Coefficient of variation (CV)0.59696858
Kurtosis-1.0884267
Mean1137.8327
Median Absolute Deviation (MAD)556
Skewness0.13322828
Sum292423
Variance461381.06
MonotonicityNot monotonic
2023-05-01T23:48:41.473590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2045 3
 
1.2%
1945 3
 
1.2%
400 3
 
1.2%
900 3
 
1.2%
540 3
 
1.2%
1543 2
 
0.8%
2335 2
 
0.8%
220 2
 
0.8%
1458 2
 
0.8%
715 2
 
0.8%
Other values (206) 232
89.9%
ValueCountFrequency (%)
0 1
0.4%
15 2
0.8%
20 1
0.4%
21 1
0.4%
22 1
0.4%
28 1
0.4%
30 1
0.4%
40 1
0.4%
50 1
0.4%
55 1
0.4%
ValueCountFrequency (%)
2350 1
0.4%
2349 2
0.8%
2345 1
0.4%
2335 2
0.8%
2325 1
0.4%
2316 1
0.4%
2315 2
0.8%
2305 1
0.4%
2304 1
0.4%
2303 1
0.4%

NATURAL
Real number (ℝ)

Distinct11
Distinct (%)4.3%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean824.5748
Minimum811
Maximum843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:41.634839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum811
5-th percentile824
Q1824
median824
Q3824
95-th percentile826.35
Maximum843
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7289103
Coefficient of variation (CV)0.0033094758
Kurtosis28.420501
Mean824.5748
Median Absolute Deviation (MAD)0
Skewness4.2154181
Sum209442
Variance7.4469515
MonotonicityNot monotonic
2023-05-01T23:48:41.800507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
824 211
81.8%
825 21
 
8.1%
826 6
 
2.3%
833 3
 
1.2%
827 3
 
1.2%
843 3
 
1.2%
835 3
 
1.2%
822 1
 
0.4%
821 1
 
0.4%
811 1
 
0.4%
(Missing) 4
 
1.6%
ValueCountFrequency (%)
811 1
 
0.4%
821 1
 
0.4%
822 1
 
0.4%
824 211
81.8%
825 21
 
8.1%
826 6
 
2.3%
827 3
 
1.2%
829 1
 
0.4%
833 3
 
1.2%
835 3
 
1.2%
ValueCountFrequency (%)
843 3
 
1.2%
835 3
 
1.2%
833 3
 
1.2%
829 1
 
0.4%
827 3
 
1.2%
826 6
 
2.3%
825 21
 
8.1%
824 211
81.8%
822 1
 
0.4%
821 1
 
0.4%

CODMUNNATU
Real number (ℝ)

Distinct93
Distinct (%)36.6%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean246555.06
Minimum110020
Maximum432290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:42.024862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum110020
5-th percentile240080
Q1240772.5
median240810
Q3241220
95-th percentile264543.5
Maximum432290
Range322270
Interquartile range (IQR)447.5

Descriptive statistics

Standard deviation27440.139
Coefficient of variation (CV)0.11129416
Kurtosis28.294831
Mean246555.06
Median Absolute Deviation (MAD)290
Skewness4.2100374
Sum62624985
Variance7.5296125 × 108
MonotonicityNot monotonic
2023-05-01T23:48:42.201326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240810 82
31.8%
241220 8
 
3.1%
240710 6
 
2.3%
241440 6
 
2.3%
240220 5
 
1.9%
240080 5
 
1.9%
241140 5
 
1.9%
241120 5
 
1.9%
240580 4
 
1.6%
240720 4
 
1.6%
Other values (83) 124
48.1%
ValueCountFrequency (%)
110020 1
 
0.4%
210840 1
 
0.4%
221100 1
 
0.4%
240000 1
 
0.4%
240010 1
 
0.4%
240020 4
1.6%
240030 1
 
0.4%
240050 1
 
0.4%
240080 5
1.9%
240100 1
 
0.4%
ValueCountFrequency (%)
432290 1
0.4%
431490 1
0.4%
430210 1
0.4%
355030 1
0.4%
353350 1
0.4%
351060 1
0.4%
330490 1
0.4%
330455 1
0.4%
330330 1
0.4%
291230 1
0.4%

DTNASC
Real number (ℝ)

Distinct258
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15585743
Minimum1011938
Maximum31101953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:42.401081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1011938
5-th percentile2120430.5
Q18341933.2
median15116940
Q323049456
95-th percentile29259933
Maximum31101953
Range30090015
Interquartile range (IQR)14707523

Descriptive statistics

Standard deviation8586402.7
Coefficient of variation (CV)0.5509139
Kurtosis-1.0958885
Mean15585743
Median Absolute Deviation (MAD)7034968.5
Skewness0.0580799
Sum4.0211218 × 109
Variance7.3726311 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:42.585558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10021937 1
 
0.4%
31101953 1
 
0.4%
3111926 1
 
0.4%
23041950 1
 
0.4%
28081934 1
 
0.4%
2121936 1
 
0.4%
19121938 1
 
0.4%
21091945 1
 
0.4%
12071934 1
 
0.4%
17101972 1
 
0.4%
Other values (248) 248
96.1%
ValueCountFrequency (%)
1011938 1
0.4%
1011979 1
0.4%
1021927 1
0.4%
1031934 1
0.4%
1091948 1
0.4%
1101942 1
0.4%
1111929 1
0.4%
1121939 1
0.4%
2021942 1
0.4%
2031937 1
0.4%
ValueCountFrequency (%)
31101953 1
0.4%
31081947 1
0.4%
31071940 1
0.4%
31051939 1
0.4%
30121959 1
0.4%
30111948 1
0.4%
30101946 1
0.4%
30091931 1
0.4%
30081937 1
0.4%
30061944 1
0.4%

IDADE
Real number (ℝ)

Distinct58
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean476.27907
Minimum422
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:42.781235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum422
5-th percentile448.55
Q1469
median479
Q3486
95-th percentile494
Maximum499
Range77
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.182785
Coefficient of variation (CV)0.029778308
Kurtosis1.5003473
Mean476.27907
Median Absolute Deviation (MAD)8
Skewness-1.1098127
Sum122880
Variance201.15139
MonotonicityNot monotonic
2023-05-01T23:48:42.973267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
484 11
 
4.3%
485 11
 
4.3%
487 11
 
4.3%
486 9
 
3.5%
479 9
 
3.5%
482 9
 
3.5%
474 8
 
3.1%
483 8
 
3.1%
490 8
 
3.1%
477 7
 
2.7%
Other values (48) 167
64.7%
ValueCountFrequency (%)
422 1
0.4%
426 1
0.4%
427 1
0.4%
432 1
0.4%
434 1
0.4%
441 2
0.8%
443 1
0.4%
444 2
0.8%
445 2
0.8%
446 1
0.4%
ValueCountFrequency (%)
499 1
 
0.4%
498 4
1.6%
496 1
 
0.4%
495 5
1.9%
494 7
2.7%
493 3
 
1.2%
492 6
2.3%
491 6
2.3%
490 8
3.1%
489 7
2.7%

SEXO
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
148 
1
110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

Length

2023-05-01T23:48:43.150571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:43.299022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

Most occurring characters

ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

Most occurring scripts

ValueCountFrequency (%)
Common 258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 148
57.4%
1 110
42.6%

RACACOR
Categorical

Distinct5
Distinct (%)2.0%
Missing13
Missing (%)5.0%
Memory size4.0 KiB
1.0
132 
4.0
98 
2.0
 
12
3.0
 
2
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters735
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 132
51.2%
4.0 98
38.0%
2.0 12
 
4.7%
3.0 2
 
0.8%
5.0 1
 
0.4%
(Missing) 13
 
5.0%

Length

2023-05-01T23:48:43.430208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:43.580529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 132
53.9%
4.0 98
40.0%
2.0 12
 
4.9%
3.0 2
 
0.8%
5.0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 245
33.3%
0 245
33.3%
1 132
18.0%
4 98
 
13.3%
2 12
 
1.6%
3 2
 
0.3%
5 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 490
66.7%
Other Punctuation 245
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 245
50.0%
1 132
26.9%
4 98
 
20.0%
2 12
 
2.4%
3 2
 
0.4%
5 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 245
33.3%
0 245
33.3%
1 132
18.0%
4 98
 
13.3%
2 12
 
1.6%
3 2
 
0.3%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 245
33.3%
0 245
33.3%
1 132
18.0%
4 98
 
13.3%
2 12
 
1.6%
3 2
 
0.3%
5 1
 
0.1%

ESTCIV
Real number (ℝ)

Distinct6
Distinct (%)2.4%
Missing11
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2.4048583
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:43.708983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5688976
Coefficient of variation (CV)0.65238672
Kurtosis8.9712914
Mean2.4048583
Median Absolute Deviation (MAD)1
Skewness2.6511443
Sum594
Variance2.4614397
MonotonicityNot monotonic
2023-05-01T23:48:43.840101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 88
34.1%
3 70
27.1%
1 65
25.2%
4 13
 
5.0%
9 9
 
3.5%
5 2
 
0.8%
(Missing) 11
 
4.3%
ValueCountFrequency (%)
1 65
25.2%
2 88
34.1%
3 70
27.1%
4 13
 
5.0%
5 2
 
0.8%
9 9
 
3.5%
ValueCountFrequency (%)
9 9
 
3.5%
5 2
 
0.8%
4 13
 
5.0%
3 70
27.1%
2 88
34.1%
1 65
25.2%

ESC
Real number (ℝ)

Distinct6
Distinct (%)2.4%
Missing12
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean3.5121951
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:43.980956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3382039
Coefficient of variation (CV)0.66573862
Kurtosis0.83205154
Mean3.5121951
Median Absolute Deviation (MAD)1
Skewness1.2176966
Sum864
Variance5.4671976
MonotonicityNot monotonic
2023-05-01T23:48:44.107242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 59
22.9%
2 57
22.1%
1 46
17.8%
3 32
12.4%
9 28
10.9%
5 24
9.3%
(Missing) 12
 
4.7%
ValueCountFrequency (%)
1 46
17.8%
2 57
22.1%
3 32
12.4%
4 59
22.9%
5 24
9.3%
9 28
10.9%
ValueCountFrequency (%)
9 28
10.9%
5 24
9.3%
4 59
22.9%
3 32
12.4%
2 57
22.1%
1 46
17.8%

ESC2010
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)2.9%
Missing13
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean2.7183673
Minimum0
Maximum9
Zeros45
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:44.251100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6808117
Coefficient of variation (CV)0.98618449
Kurtosis0.80150056
Mean2.7183673
Median Absolute Deviation (MAD)1
Skewness1.2872748
Sum666
Variance7.1867514
MonotonicityNot monotonic
2023-05-01T23:48:44.381123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 59
22.9%
3 58
22.5%
0 45
17.4%
2 31
12.0%
9 28
10.9%
5 23
 
8.9%
4 1
 
0.4%
(Missing) 13
 
5.0%
ValueCountFrequency (%)
0 45
17.4%
1 59
22.9%
2 31
12.0%
3 58
22.5%
4 1
 
0.4%
5 23
 
8.9%
9 28
10.9%
ValueCountFrequency (%)
9 28
10.9%
5 23
 
8.9%
4 1
 
0.4%
3 58
22.5%
2 31
12.0%
1 59
22.9%
0 45
17.4%

SERIESCFAL
Categorical

Distinct3
Distinct (%)75.0%
Missing254
Missing (%)98.4%
Memory size4.0 KiB
4.0
2 
8.0
1 
3.0
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row4.0
2nd row8.0
3rd row3.0
4th row4.0

Common Values

ValueCountFrequency (%)
4.0 2
 
0.8%
8.0 1
 
0.4%
3.0 1
 
0.4%
(Missing) 254
98.4%

Length

2023-05-01T23:48:44.522618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:44.682327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 2
50.0%
8.0 1
25.0%
3.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
4 2
16.7%
8 1
 
8.3%
3 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
4 2
25.0%
8 1
 
12.5%
3 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
4 2
16.7%
8 1
 
8.3%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
4 2
16.7%
8 1
 
8.3%
3 1
 
8.3%

OCUP
Real number (ℝ)

Distinct55
Distinct (%)25.9%
Missing46
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean735270.62
Minimum10315
Maximum999994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:44.910213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10315
5-th percentile214293
Q1512933.75
median782310
Q3999993
95-th percentile999993
Maximum999994
Range989679
Interquartile range (IQR)487059.25

Descriptive statistics

Standard deviation293735.18
Coefficient of variation (CV)0.39949262
Kurtosis-1.0365453
Mean735270.62
Median Absolute Deviation (MAD)217683
Skewness-0.60511738
Sum1.5587737 × 108
Variance8.6280358 × 1010
MonotonicityNot monotonic
2023-05-01T23:48:45.176148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999993 56
21.7%
999992 45
17.4%
622020 13
 
5.0%
141410 7
 
2.7%
354705 7
 
2.7%
763015 6
 
2.3%
715210 5
 
1.9%
333115 5
 
1.9%
411010 5
 
1.9%
512105 4
 
1.6%
Other values (45) 59
22.9%
(Missing) 46
17.8%
ValueCountFrequency (%)
10315 1
 
0.4%
111345 1
 
0.4%
141410 7
2.7%
214110 1
 
0.4%
214205 1
 
0.4%
214365 1
 
0.4%
223129 1
 
0.4%
241335 1
 
0.4%
251605 2
 
0.8%
254205 1
 
0.4%
ValueCountFrequency (%)
999994 1
 
0.4%
999993 56
21.7%
999992 45
17.4%
783210 1
 
0.4%
782510 1
 
0.4%
782315 2
 
0.8%
782305 2
 
0.8%
774105 1
 
0.4%
763015 6
 
2.3%
760310 1
 
0.4%

CODMUNRES
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
240810
258 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240810
2nd row240810
3rd row240810
4th row240810
5th row240810

Common Values

ValueCountFrequency (%)
240810 258
100.0%

Length

2023-05-01T23:48:45.415542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:45.555371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
240810 258
100.0%

Most occurring characters

ValueCountFrequency (%)
0 516
33.3%
2 258
16.7%
4 258
16.7%
8 258
16.7%
1 258
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 516
33.3%
2 258
16.7%
4 258
16.7%
8 258
16.7%
1 258
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 516
33.3%
2 258
16.7%
4 258
16.7%
8 258
16.7%
1 258
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 516
33.3%
2 258
16.7%
4 258
16.7%
8 258
16.7%
1 258
16.7%

LOCOCOR
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
214 
2
36 
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

Length

2023-05-01T23:48:45.673019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:45.824167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 214
82.9%
2 36
 
14.0%
3 8
 
3.1%

CODESTAB
Real number (ℝ)

Distinct23
Distinct (%)9.2%
Missing8
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean3387223.9
Minimum104515
Maximum9361936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:45.957283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum104515
5-th percentile282715
Q12408260
median2656930
Q34013484
95-th percentile8003629
Maximum9361936
Range9257421
Interquartile range (IQR)1605224

Descriptive statistics

Standard deviation2594220.3
Coefficient of variation (CV)0.76588393
Kurtosis-0.36726807
Mean3387223.9
Median Absolute Deviation (MAD)1356554
Skewness0.77159985
Sum8.4680596 × 108
Variance6.7299791 × 1012
MonotonicityNot monotonic
2023-05-01T23:48:46.106003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
282715 54
20.9%
2656930 36
14.0%
7408765 18
 
7.0%
4013484 18
 
7.0%
3708926 16
 
6.2%
2798727 15
 
5.8%
2654024 15
 
5.8%
2654016 14
 
5.4%
2408260 13
 
5.0%
8003629 12
 
4.7%
Other values (13) 39
15.1%
ValueCountFrequency (%)
104515 1
 
0.4%
282715 54
20.9%
2408252 1
 
0.4%
2408260 13
 
5.0%
2408570 1
 
0.4%
2409194 6
 
2.3%
2653923 2
 
0.8%
2653982 1
 
0.4%
2654016 14
 
5.4%
2654024 15
 
5.8%
ValueCountFrequency (%)
9361936 7
 
2.7%
8003629 12
4.7%
7923287 10
3.9%
7885199 1
 
0.4%
7408765 18
7.0%
6531288 5
 
1.9%
4014235 1
 
0.4%
4013484 18
7.0%
3708926 16
6.2%
2798727 15
5.8%

CODMUNOCOR
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
240810
256 
240325
 
1
241200
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1548
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row240810
2nd row240810
3rd row240810
4th row240810
5th row240810

Common Values

ValueCountFrequency (%)
240810 256
99.2%
240325 1
 
0.4%
241200 1
 
0.4%

Length

2023-05-01T23:48:46.253890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:46.404424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
240810 256
99.2%
240325 1
 
0.4%
241200 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 515
33.3%
2 260
16.8%
4 258
16.7%
1 257
16.6%
8 256
16.5%
3 1
 
0.1%
5 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 515
33.3%
2 260
16.8%
4 258
16.7%
1 257
16.6%
8 256
16.5%
3 1
 
0.1%
5 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 515
33.3%
2 260
16.8%
4 258
16.7%
1 257
16.6%
8 256
16.5%
3 1
 
0.1%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 515
33.3%
2 260
16.8%
4 258
16.7%
1 257
16.6%
8 256
16.5%
3 1
 
0.1%
5 1
 
0.1%

IDADEMAE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

ESCMAE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

ESCMAE2010
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

SERIESCMAE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

OCUPMAE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

QTDFILVIVO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

QTDFILMORT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

GRAVIDEZ
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

SEMAGESTAC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

GESTACAO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

PARTO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

OBITOPARTO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

PESO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

TPMORTEOCO
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing247
Missing (%)95.7%
Memory size4.0 KiB
8.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 11
 
4.3%
(Missing) 247
95.7%

Length

2023-05-01T23:48:46.542542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:46.686671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 11
100.0%

Most occurring characters

ValueCountFrequency (%)
8 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 11
50.0%
0 11
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 11
33.3%
. 11
33.3%
0 11
33.3%

OBITOGRAV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing247
Missing (%)95.7%
Memory size4.0 KiB
2.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 11
 
4.3%
(Missing) 247
95.7%

Length

2023-05-01T23:48:46.804151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:46.942667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 11
100.0%

Most occurring characters

ValueCountFrequency (%)
2 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11
50.0%
0 11
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11
33.3%
. 11
33.3%
0 11
33.3%

OBITOPUERP
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing247
Missing (%)95.7%
Memory size4.0 KiB
3.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 11
 
4.3%
(Missing) 247
95.7%

Length

2023-05-01T23:48:47.059018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:47.198943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 11
100.0%

Most occurring characters

ValueCountFrequency (%)
3 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11
50.0%
0 11
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11
33.3%
. 11
33.3%
0 11
33.3%

ASSISTMED
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)2.4%
Missing135
Missing (%)52.3%
Memory size4.0 KiB
1.0
117 
2.0
 
5
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 117
45.3%
2.0 5
 
1.9%
9.0 1
 
0.4%
(Missing) 135
52.3%

Length

2023-05-01T23:48:47.316778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:47.476603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 117
95.1%
2.0 5
 
4.1%
9.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 117
31.7%
2 5
 
1.4%
9 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 246
66.7%
Other Punctuation 123
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
50.0%
1 117
47.6%
2 5
 
2.0%
9 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 369
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 117
31.7%
2 5
 
1.4%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 117
31.7%
2 5
 
1.4%
9 1
 
0.3%

EXAME
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing257
Missing (%)99.6%
Memory size4.0 KiB
2.0
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
0.4%
(Missing) 257
99.6%

Length

2023-05-01T23:48:47.605179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:47.748693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

CIRURGIA
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing257
Missing (%)99.6%
Memory size4.0 KiB
2.0
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
0.4%
(Missing) 257
99.6%

Length

2023-05-01T23:48:47.863400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:48.007074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

NECROPSIA
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)2.5%
Missing138
Missing (%)53.5%
Memory size4.0 KiB
2.0
115 
9.0
 
4
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters360
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 115
44.6%
9.0 4
 
1.6%
1.0 1
 
0.4%
(Missing) 138
53.5%

Length

2023-05-01T23:48:48.125177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:48.286013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 115
95.8%
9.0 4
 
3.3%
1.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 120
33.3%
0 120
33.3%
2 115
31.9%
9 4
 
1.1%
1 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
66.7%
Other Punctuation 120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
50.0%
2 115
47.9%
9 4
 
1.7%
1 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 120
33.3%
0 120
33.3%
2 115
31.9%
9 4
 
1.1%
1 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 120
33.3%
0 120
33.3%
2 115
31.9%
9 4
 
1.1%
1 1
 
0.3%

LINHAA
Categorical

Distinct32
Distinct (%)13.6%
Missing23
Missing (%)8.9%
Memory size4.0 KiB
*A419
79 
*J960
36 
*B342*U071
18 
*B342*U071*U049
14 
*R570
12 
Other values (27)
76 

Length

Max length15
Median length5
Mean length6.0851064
Min length5

Characters and Unicode

Total characters1430
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)5.1%

Sample

1st row*J960
2nd row*R570
3rd row*B342*U071*U049
4th row*R578
5th row*R579

Common Values

ValueCountFrequency (%)
*A419 79
30.6%
*J960 36
14.0%
*B342*U071 18
 
7.0%
*B342*U071*U049 14
 
5.4%
*R570 12
 
4.7%
*J969 11
 
4.3%
*J984 9
 
3.5%
*R092 6
 
2.3%
*R688 5
 
1.9%
*R090 5
 
1.9%
Other values (22) 40
15.5%
(Missing) 23
 
8.9%

Length

2023-05-01T23:48:48.475095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a419 79
33.6%
j960 36
15.3%
b342*u071 18
 
7.7%
b342*u071*u049 14
 
6.0%
r570 12
 
5.1%
j969 11
 
4.7%
j984 9
 
3.8%
r092 6
 
2.6%
r688 5
 
2.1%
r090 5
 
2.1%
Other values (22) 40
17.0%

Most occurring characters

ValueCountFrequency (%)
* 286
20.0%
9 193
13.5%
4 142
9.9%
1 130
9.1%
0 119
8.3%
A 80
 
5.6%
J 72
 
5.0%
7 62
 
4.3%
6 58
 
4.1%
2 52
 
3.6%
Other values (11) 236
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 855
59.8%
Uppercase Letter 289
 
20.2%
Other Punctuation 286
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 193
22.6%
4 142
16.6%
1 130
15.2%
0 119
13.9%
7 62
 
7.3%
6 58
 
6.8%
2 52
 
6.1%
8 37
 
4.3%
3 36
 
4.2%
5 26
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 80
27.7%
J 72
24.9%
U 51
17.6%
R 38
13.1%
B 36
12.5%
N 3
 
1.0%
X 3
 
1.0%
I 3
 
1.0%
E 2
 
0.7%
M 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
* 286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1141
79.8%
Latin 289
 
20.2%

Most frequent character per script

Common
ValueCountFrequency (%)
* 286
25.1%
9 193
16.9%
4 142
12.4%
1 130
11.4%
0 119
10.4%
7 62
 
5.4%
6 58
 
5.1%
2 52
 
4.6%
8 37
 
3.2%
3 36
 
3.2%
Latin
ValueCountFrequency (%)
A 80
27.7%
J 72
24.9%
U 51
17.6%
R 38
13.1%
B 36
12.5%
N 3
 
1.0%
X 3
 
1.0%
I 3
 
1.0%
E 2
 
0.7%
M 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 286
20.0%
9 193
13.5%
4 142
9.9%
1 130
9.1%
0 119
8.3%
A 80
 
5.6%
J 72
 
5.0%
7 62
 
4.3%
6 58
 
4.1%
2 52
 
3.6%
Other values (11) 236
16.5%

LINHAB
Categorical

Distinct42
Distinct (%)18.6%
Missing32
Missing (%)12.4%
Memory size4.0 KiB
*B342*U071
46 
*J189
27 
*A419
22 
*B342*U071*U049
16 
*J984
15 
Other values (37)
100 

Length

Max length15
Median length5
Mean length7.0132743
Min length5

Characters and Unicode

Total characters1585
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)9.7%

Sample

1st row*B342*U071
2nd row*A419
3rd row*E119
4th row*A419
5th row*B342*U071*U049

Common Values

ValueCountFrequency (%)
*B342*U071 46
17.8%
*J189 27
10.5%
*A419 22
8.5%
*B342*U071*U049 16
 
6.2%
*J984 15
 
5.8%
*J159 15
 
5.8%
*J128 9
 
3.5%
*J129 8
 
3.1%
*N179 7
 
2.7%
*J960 7
 
2.7%
Other values (32) 54
20.9%
(Missing) 32
12.4%

Length

2023-05-01T23:48:48.700936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b342*u071 46
20.4%
j189 27
11.9%
a419 22
9.7%
b342*u071*u049 16
 
7.1%
j984 15
 
6.6%
j159 15
 
6.6%
j128 9
 
4.0%
j129 8
 
3.5%
n179 7
 
3.1%
j960 7
 
3.1%
Other values (32) 54
23.9%

Most occurring characters

ValueCountFrequency (%)
* 317
20.0%
1 173
10.9%
9 148
9.3%
4 133
8.4%
0 123
 
7.8%
2 108
 
6.8%
J 97
 
6.1%
U 90
 
5.7%
3 79
 
5.0%
7 79
 
5.0%
Other values (14) 238
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 942
59.4%
Uppercase Letter 326
 
20.6%
Other Punctuation 317
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 97
29.8%
U 90
27.6%
B 71
21.8%
A 22
 
6.7%
N 11
 
3.4%
I 10
 
3.1%
X 9
 
2.8%
R 5
 
1.5%
L 5
 
1.5%
C 2
 
0.6%
Other values (3) 4
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 173
18.4%
9 148
15.7%
4 133
14.1%
0 123
13.1%
2 108
11.5%
3 79
8.4%
7 79
8.4%
8 67
 
7.1%
5 19
 
2.0%
6 13
 
1.4%
Other Punctuation
ValueCountFrequency (%)
* 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1259
79.4%
Latin 326
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 97
29.8%
U 90
27.6%
B 71
21.8%
A 22
 
6.7%
N 11
 
3.4%
I 10
 
3.1%
X 9
 
2.8%
R 5
 
1.5%
L 5
 
1.5%
C 2
 
0.6%
Other values (3) 4
 
1.2%
Common
ValueCountFrequency (%)
* 317
25.2%
1 173
13.7%
9 148
11.8%
4 133
10.6%
0 123
 
9.8%
2 108
 
8.6%
3 79
 
6.3%
7 79
 
6.3%
8 67
 
5.3%
5 19
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 317
20.0%
1 173
10.9%
9 148
9.3%
4 133
8.4%
0 123
 
7.8%
2 108
 
6.8%
J 97
 
6.1%
U 90
 
5.7%
3 79
 
5.0%
7 79
 
5.0%
Other values (14) 238
15.0%

LINHAC
Categorical

Distinct31
Distinct (%)19.0%
Missing95
Missing (%)36.8%
Memory size4.0 KiB
*B342*U071
65 
*B342*U072
22 
*B342*U071*U049
14 
*J189
13 
*J159
 
5
Other values (26)
44 

Length

Max length15
Median length10
Mean length8.8343558
Min length5

Characters and Unicode

Total characters1440
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)10.4%

Sample

1st row*A419
2nd row*B342*U071*U049
3rd row*B342*U072
4th row*J189
5th row*J159

Common Values

ValueCountFrequency (%)
*B342*U071 65
25.2%
*B342*U072 22
 
8.5%
*B342*U071*U049 14
 
5.4%
*J189 13
 
5.0%
*J159 5
 
1.9%
*J128 5
 
1.9%
*B342*U072*U049 4
 
1.6%
*J960 4
 
1.6%
*A419 3
 
1.2%
*N179 3
 
1.2%
Other values (21) 25
 
9.7%
(Missing) 95
36.8%

Length

2023-05-01T23:48:48.911682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b342*u071 65
39.9%
b342*u072 22
 
13.5%
b342*u071*u049 14
 
8.6%
j189 13
 
8.0%
j159 5
 
3.1%
j128 5
 
3.1%
b342*u072*u049 4
 
2.5%
j960 4
 
2.5%
a419 3
 
1.8%
n179 3
 
1.8%
Other values (21) 25
 
15.3%

Most occurring characters

ValueCountFrequency (%)
* 288
20.0%
2 146
10.1%
0 138
9.6%
4 132
9.2%
U 123
8.5%
1 116
8.1%
3 110
 
7.6%
7 110
 
7.6%
B 105
 
7.3%
9 59
 
4.1%
Other values (14) 113
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 859
59.7%
Uppercase Letter 293
 
20.3%
Other Punctuation 288
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 123
42.0%
B 105
35.8%
J 35
 
11.9%
I 7
 
2.4%
N 5
 
1.7%
X 5
 
1.7%
A 3
 
1.0%
R 2
 
0.7%
K 2
 
0.7%
Y 2
 
0.7%
Other values (3) 4
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 146
17.0%
0 138
16.1%
4 132
15.4%
1 116
13.5%
3 110
12.8%
7 110
12.8%
9 59
6.9%
8 32
 
3.7%
6 9
 
1.0%
5 7
 
0.8%
Other Punctuation
ValueCountFrequency (%)
* 288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1147
79.7%
Latin 293
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 123
42.0%
B 105
35.8%
J 35
 
11.9%
I 7
 
2.4%
N 5
 
1.7%
X 5
 
1.7%
A 3
 
1.0%
R 2
 
0.7%
K 2
 
0.7%
Y 2
 
0.7%
Other values (3) 4
 
1.4%
Common
ValueCountFrequency (%)
* 288
25.1%
2 146
12.7%
0 138
12.0%
4 132
11.5%
1 116
10.1%
3 110
 
9.6%
7 110
 
9.6%
9 59
 
5.1%
8 32
 
2.8%
6 9
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 288
20.0%
2 146
10.1%
0 138
9.6%
4 132
9.2%
U 123
8.5%
1 116
8.1%
3 110
 
7.6%
7 110
 
7.6%
B 105
 
7.3%
9 59
 
4.1%
Other values (14) 113
 
7.8%

LINHAD
Categorical

Distinct26
Distinct (%)37.7%
Missing189
Missing (%)73.3%
Memory size4.0 KiB
*B342*U071
33 
*B342*U071*U049
6 
*B342*U072
5 
*I350
 
2
*E149
 
2
Other values (21)
21 

Length

Max length15
Median length10
Mean length9.1304348
Min length5

Characters and Unicode

Total characters630
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)30.4%

Sample

1st row*B342*U071
2nd row*E119
3rd row*B342*U071
4th row*B342*U071
5th row*B342*U071

Common Values

ValueCountFrequency (%)
*B342*U071 33
 
12.8%
*B342*U071*U049 6
 
2.3%
*B342*U072 5
 
1.9%
*I350 2
 
0.8%
*E149 2
 
0.8%
*N19X 1
 
0.4%
*N179 1
 
0.4%
*J841 1
 
0.4%
*B342*U071*C349 1
 
0.4%
*J188 1
 
0.4%
Other values (16) 16
 
6.2%
(Missing) 189
73.3%

Length

2023-05-01T23:48:49.118986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b342*u071 33
47.8%
b342*u071*u049 6
 
8.7%
b342*u072 5
 
7.2%
i350 2
 
2.9%
e149 2
 
2.9%
r418 1
 
1.4%
e119 1
 
1.4%
r54x 1
 
1.4%
e669 1
 
1.4%
k919 1
 
1.4%
Other values (16) 16
23.2%

Most occurring characters

ValueCountFrequency (%)
* 126
20.0%
4 62
9.8%
0 59
9.4%
2 56
8.9%
1 55
8.7%
3 53
8.4%
U 53
8.4%
7 48
 
7.6%
B 46
 
7.3%
9 22
 
3.5%
Other values (15) 50
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 373
59.2%
Uppercase Letter 131
 
20.8%
Other Punctuation 126
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 53
40.5%
B 46
35.1%
X 5
 
3.8%
E 4
 
3.1%
N 4
 
3.1%
I 4
 
3.1%
J 3
 
2.3%
C 3
 
2.3%
K 2
 
1.5%
M 2
 
1.5%
Other values (4) 5
 
3.8%
Decimal Number
ValueCountFrequency (%)
4 62
16.6%
0 59
15.8%
2 56
15.0%
1 55
14.7%
3 53
14.2%
7 48
12.9%
9 22
 
5.9%
8 9
 
2.4%
6 6
 
1.6%
5 3
 
0.8%
Other Punctuation
ValueCountFrequency (%)
* 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 499
79.2%
Latin 131
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 53
40.5%
B 46
35.1%
X 5
 
3.8%
E 4
 
3.1%
N 4
 
3.1%
I 4
 
3.1%
J 3
 
2.3%
C 3
 
2.3%
K 2
 
1.5%
M 2
 
1.5%
Other values (4) 5
 
3.8%
Common
ValueCountFrequency (%)
* 126
25.3%
4 62
12.4%
0 59
11.8%
2 56
11.2%
1 55
11.0%
3 53
10.6%
7 48
 
9.6%
9 22
 
4.4%
8 9
 
1.8%
6 6
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 126
20.0%
4 62
9.8%
0 59
9.4%
2 56
8.9%
1 55
8.7%
3 53
8.4%
U 53
8.4%
7 48
 
7.6%
B 46
 
7.3%
9 22
 
3.5%
Other values (15) 50
 
7.9%

LINHAII
Categorical

HIGH CARDINALITY  MISSING 

Distinct108
Distinct (%)65.1%
Missing92
Missing (%)35.7%
Memory size4.0 KiB
*G309
 
12
*I10X*E149
 
11
*G20X
 
6
*I10X
 
6
*R54X
 
5
Other values (103)
126 

Length

Max length20
Median length10
Mean length8.1325301
Min length5

Characters and Unicode

Total characters1350
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)51.8%

Sample

1st row*J690*G20X
2nd row*I694*I10X
3rd row*G20X
4th row*G20X
5th row*N039*I509

Common Values

ValueCountFrequency (%)
*G309 12
 
4.7%
*I10X*E149 11
 
4.3%
*G20X 6
 
2.3%
*I10X 6
 
2.3%
*R54X 5
 
1.9%
*N179 4
 
1.6%
*I10X*G309 3
 
1.2%
*I10X*E119 3
 
1.2%
*C61X 3
 
1.2%
*E149*I10X 3
 
1.2%
Other values (98) 110
42.6%
(Missing) 92
35.7%

Length

2023-05-01T23:48:49.336755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g309 12
 
7.2%
i10x*e149 11
 
6.6%
g20x 6
 
3.6%
i10x 6
 
3.6%
r54x 5
 
3.0%
n179 4
 
2.4%
i10x*g309 3
 
1.8%
i10x*e119 3
 
1.8%
c61x 3
 
1.8%
e149*i10x 3
 
1.8%
Other values (98) 110
66.3%

Most occurring characters

ValueCountFrequency (%)
* 270
20.0%
0 138
10.2%
9 138
10.2%
1 138
10.2%
X 105
 
7.8%
I 102
 
7.6%
4 76
 
5.6%
3 52
 
3.9%
6 44
 
3.3%
E 42
 
3.1%
Other values (16) 245
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 705
52.2%
Uppercase Letter 375
27.8%
Other Punctuation 270
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 105
28.0%
I 102
27.2%
E 42
 
11.2%
G 27
 
7.2%
C 23
 
6.1%
N 21
 
5.6%
F 13
 
3.5%
R 12
 
3.2%
J 12
 
3.2%
M 6
 
1.6%
Other values (5) 12
 
3.2%
Decimal Number
ValueCountFrequency (%)
0 138
19.6%
9 138
19.6%
1 138
19.6%
4 76
10.8%
3 52
 
7.4%
6 44
 
6.2%
5 40
 
5.7%
2 33
 
4.7%
8 24
 
3.4%
7 22
 
3.1%
Other Punctuation
ValueCountFrequency (%)
* 270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 975
72.2%
Latin 375
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 105
28.0%
I 102
27.2%
E 42
 
11.2%
G 27
 
7.2%
C 23
 
6.1%
N 21
 
5.6%
F 13
 
3.5%
R 12
 
3.2%
J 12
 
3.2%
M 6
 
1.6%
Other values (5) 12
 
3.2%
Common
ValueCountFrequency (%)
* 270
27.7%
0 138
14.2%
9 138
14.2%
1 138
14.2%
4 76
 
7.8%
3 52
 
5.3%
6 44
 
4.5%
5 40
 
4.1%
2 33
 
3.4%
8 24
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 270
20.0%
0 138
10.2%
9 138
10.2%
1 138
10.2%
X 105
 
7.8%
I 102
 
7.6%
4 76
 
5.6%
3 52
 
3.9%
6 44
 
3.3%
E 42
 
3.1%
Other values (16) 245
18.1%

CAUSABAS
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
B342
258 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1032
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB342
2nd rowB342
3rd rowB342
4th rowB342
5th rowB342

Common Values

ValueCountFrequency (%)
B342 258
100.0%

Length

2023-05-01T23:48:49.565303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:49.768943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
b342 258
100.0%

Most occurring characters

ValueCountFrequency (%)
B 258
25.0%
3 258
25.0%
4 258
25.0%
2 258
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 774
75.0%
Uppercase Letter 258
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 258
33.3%
4 258
33.3%
2 258
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 774
75.0%
Latin 258
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 258
33.3%
4 258
33.3%
2 258
33.3%
Latin
ValueCountFrequency (%)
B 258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 258
25.0%
3 258
25.0%
4 258
25.0%
2 258
25.0%

CB_PRE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

COMUNSVOIM
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)12.5%
Missing250
Missing (%)96.9%
Memory size4.0 KiB
240810.0
8 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters64
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240810.0
2nd row240810.0
3rd row240810.0
4th row240810.0
5th row240810.0

Common Values

ValueCountFrequency (%)
240810.0 8
 
3.1%
(Missing) 250
96.9%

Length

2023-05-01T23:48:49.949356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:50.193978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
240810.0 8
100.0%

Most occurring characters

ValueCountFrequency (%)
0 24
37.5%
2 8
 
12.5%
4 8
 
12.5%
8 8
 
12.5%
1 8
 
12.5%
. 8
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
87.5%
Other Punctuation 8
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24
42.9%
2 8
 
14.3%
4 8
 
14.3%
8 8
 
14.3%
1 8
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24
37.5%
2 8
 
12.5%
4 8
 
12.5%
8 8
 
12.5%
1 8
 
12.5%
. 8
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
37.5%
2 8
 
12.5%
4 8
 
12.5%
8 8
 
12.5%
1 8
 
12.5%
. 8
 
12.5%

DTATESTADO
Real number (ℝ)

Distinct111
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15520084
Minimum1012022
Maximum31072022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:50.421970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1012022
5-th percentile1030522
Q16024522
median15022022
Q324774522
95-th percentile30012022
Maximum31072022
Range30060000
Interquartile range (IQR)18750000

Descriptive statistics

Standard deviation9574332.8
Coefficient of variation (CV)0.61689955
Kurtosis-1.3830857
Mean15520084
Median Absolute Deviation (MAD)9000000
Skewness0.031867967
Sum4.0041817 × 109
Variance9.1667848 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:50.689083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1022022 10
 
3.9%
6022022 8
 
3.1%
27012022 8
 
3.1%
4022022 8
 
3.1%
25012022 7
 
2.7%
31012022 7
 
2.7%
4072022 5
 
1.9%
12022022 5
 
1.9%
15022022 5
 
1.9%
3022022 5
 
1.9%
Other values (101) 190
73.6%
ValueCountFrequency (%)
1012022 3
 
1.2%
1022022 10
3.9%
1032022 1
 
0.4%
1072022 1
 
0.4%
2012022 1
 
0.4%
2022022 4
 
1.6%
2032022 1
 
0.4%
2092022 1
 
0.4%
3012022 2
 
0.8%
3022022 5
1.9%
ValueCountFrequency (%)
31072022 1
 
0.4%
31052022 1
 
0.4%
31012022 7
2.7%
30072022 1
 
0.4%
30052022 1
 
0.4%
30032022 1
 
0.4%
30012022 3
1.2%
29072022 1
 
0.4%
29062022 2
 
0.8%
29012022 3
1.2%

CIRCOBITO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

ACIDTRAB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

FONTE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

NUMEROLOTE
Real number (ℝ)

Distinct36
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220022
Minimum20220004
Maximum20220054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:50.948195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20220004
5-th percentile20220008
Q120220016
median20220019
Q320220024
95-th percentile20220041
Maximum20220054
Range50
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.888112
Coefficient of variation (CV)5.384817 × 10-7
Kurtosis0.39644288
Mean20220022
Median Absolute Deviation (MAD)4
Skewness0.99021413
Sum5.2167656 × 109
Variance118.55098
MonotonicityNot monotonic
2023-05-01T23:48:51.196290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
20220020 28
 
10.9%
20220017 28
 
10.9%
20220021 26
 
10.1%
20220011 22
 
8.5%
20220019 18
 
7.0%
20220040 14
 
5.4%
20220008 12
 
4.7%
20220016 11
 
4.3%
20220009 11
 
4.3%
20220018 10
 
3.9%
Other values (26) 78
30.2%
ValueCountFrequency (%)
20220004 1
 
0.4%
20220005 3
 
1.2%
20220007 1
 
0.4%
20220008 12
4.7%
20220009 11
4.3%
20220010 1
 
0.4%
20220011 22
8.5%
20220013 1
 
0.4%
20220014 3
 
1.2%
20220015 8
 
3.1%
ValueCountFrequency (%)
20220054 5
 
1.9%
20220049 1
 
0.4%
20220047 1
 
0.4%
20220044 2
 
0.8%
20220043 3
 
1.2%
20220041 3
 
1.2%
20220040 14
5.4%
20220039 6
2.3%
20220038 1
 
0.4%
20220037 2
 
0.8%

DTINVESTIG
Real number (ℝ)

Distinct81
Distinct (%)54.0%
Missing108
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean14574889
Minimum1022022
Maximum31052022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:51.453939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1022022
5-th percentile2026522
Q17034522
median14022022
Q322022022
95-th percentile28057522
Maximum31052022
Range30030000
Interquartile range (IQR)14987500

Descriptive statistics

Standard deviation8744530.5
Coefficient of variation (CV)0.59997237
Kurtosis-1.1699413
Mean14574889
Median Absolute Deviation (MAD)7520000
Skewness0.20364457
Sum2.1862333 × 109
Variance7.6466814 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:51.708745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10022022 8
 
3.1%
4022022 7
 
2.7%
6022022 6
 
2.3%
15022022 6
 
2.3%
11022022 4
 
1.6%
12022022 4
 
1.6%
8022022 3
 
1.2%
4072022 3
 
1.2%
16022022 3
 
1.2%
2022022 3
 
1.2%
Other values (71) 103
39.9%
(Missing) 108
41.9%
ValueCountFrequency (%)
1022022 2
 
0.8%
1032022 1
 
0.4%
1082022 1
 
0.4%
1092022 1
 
0.4%
2022022 3
1.2%
2032022 1
 
0.4%
2092022 2
 
0.8%
3022022 2
 
0.8%
3032022 1
 
0.4%
4022022 7
2.7%
ValueCountFrequency (%)
31052022 1
0.4%
31012022 2
0.8%
30032022 1
0.4%
30012022 1
0.4%
29062022 2
0.8%
28062022 1
0.4%
28052022 1
0.4%
28032022 1
0.4%
28022022 2
0.8%
28012022 1
0.4%

DTCADASTRO
Real number (ℝ)

Distinct78
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16111944
Minimum1042022
Maximum31032022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:51.968800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1042022
5-th percentile5082022
Q19032022
median17022022
Q322022022
95-th percentile28032022
Maximum31032022
Range29990000
Interquartile range (IQR)12990000

Descriptive statistics

Standard deviation7492397.1
Coefficient of variation (CV)0.46502129
Kurtosis-1.0581199
Mean16111944
Median Absolute Deviation (MAD)6030000
Skewness0.10194503
Sum4.1568817 × 109
Variance5.6136015 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:52.235006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8032022 15
 
5.8%
11042022 13
 
5.0%
7042022 12
 
4.7%
9032022 9
 
3.5%
6042022 9
 
3.5%
19042022 9
 
3.5%
13042022 9
 
3.5%
12042022 8
 
3.1%
18042022 8
 
3.1%
25042022 7
 
2.7%
Other values (68) 159
61.6%
ValueCountFrequency (%)
1042022 1
 
0.4%
1092022 1
 
0.4%
2092022 1
 
0.4%
3022022 1
 
0.4%
4032022 1
 
0.4%
4042022 1
 
0.4%
4072022 1
 
0.4%
5042022 3
1.2%
5072022 2
0.8%
5082022 2
0.8%
ValueCountFrequency (%)
31032022 5
1.9%
30032022 2
 
0.8%
29082022 1
 
0.4%
29072022 1
 
0.4%
29032022 2
 
0.8%
28062022 1
 
0.4%
28032022 5
1.9%
28012022 4
1.6%
27072022 1
 
0.4%
27042022 2
 
0.8%

ATESTANTE
Categorical

Distinct5
Distinct (%)2.2%
Missing26
Missing (%)10.1%
Memory size4.0 KiB
1.0
103 
2.0
66 
5.0
55 
4.0
 
6
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters696
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 103
39.9%
2.0 66
25.6%
5.0 55
21.3%
4.0 6
 
2.3%
3.0 2
 
0.8%
(Missing) 26
 
10.1%

Length

2023-05-01T23:48:52.469528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:52.682204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 103
44.4%
2.0 66
28.4%
5.0 55
23.7%
4.0 6
 
2.6%
3.0 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 232
33.3%
0 232
33.3%
1 103
14.8%
2 66
 
9.5%
5 55
 
7.9%
4 6
 
0.9%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 464
66.7%
Other Punctuation 232
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 232
50.0%
1 103
22.2%
2 66
 
14.2%
5 55
 
11.9%
4 6
 
1.3%
3 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 232
33.3%
0 232
33.3%
1 103
14.8%
2 66
 
9.5%
5 55
 
7.9%
4 6
 
0.9%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 232
33.3%
0 232
33.3%
1 103
14.8%
2 66
 
9.5%
5 55
 
7.9%
4 6
 
0.9%
3 2
 
0.3%

STCODIFICA
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
S
258 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 258
100.0%

Length

2023-05-01T23:48:52.879855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:53.074116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 258
100.0%

Most occurring characters

ValueCountFrequency (%)
S 258
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 258
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 258
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 258
100.0%

CODIFICADO
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
S
258 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 258
100.0%

Length

2023-05-01T23:48:53.233473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:53.422880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 258
100.0%

Most occurring characters

ValueCountFrequency (%)
S 258
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 258
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 258
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 258
100.0%

VERSAOSIST
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3.2.30
258 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1548
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.2.30
2nd row3.2.30
3rd row3.2.30
4th row3.2.30
5th row3.2.30

Common Values

ValueCountFrequency (%)
3.2.30 258
100.0%

Length

2023-05-01T23:48:53.580311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:53.732288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.2.30 258
100.0%

Most occurring characters

ValueCountFrequency (%)
3 516
33.3%
. 516
33.3%
2 258
16.7%
0 258
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1032
66.7%
Other Punctuation 516
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 516
50.0%
2 258
25.0%
0 258
25.0%
Other Punctuation
ValueCountFrequency (%)
. 516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 516
33.3%
. 516
33.3%
2 258
16.7%
0 258
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 516
33.3%
. 516
33.3%
2 258
16.7%
0 258
16.7%

VERSAOSCB
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3.4
258 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters774
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.4
2nd row3.4
3rd row3.4
4th row3.4
5th row3.4

Common Values

ValueCountFrequency (%)
3.4 258
100.0%

Length

2023-05-01T23:48:53.847440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:53.989427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.4 258
100.0%

Most occurring characters

ValueCountFrequency (%)
3 258
33.3%
. 258
33.3%
4 258
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 516
66.7%
Other Punctuation 258
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 258
50.0%
4 258
50.0%
Other Punctuation
ValueCountFrequency (%)
. 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 258
33.3%
. 258
33.3%
4 258
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 258
33.3%
. 258
33.3%
4 258
33.3%

FONTEINV
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)3.2%
Missing100
Missing (%)38.8%
Memory size4.0 KiB
7.0
143 
8.0
 
10
2.0
 
3
3.0
 
1
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters474
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row8.0
2nd row8.0
3rd row7.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 143
55.4%
8.0 10
 
3.9%
2.0 3
 
1.2%
3.0 1
 
0.4%
9.0 1
 
0.4%
(Missing) 100
38.8%

Length

2023-05-01T23:48:54.103589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:54.273599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7.0 143
90.5%
8.0 10
 
6.3%
2.0 3
 
1.9%
3.0 1
 
0.6%
9.0 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 158
33.3%
0 158
33.3%
7 143
30.2%
8 10
 
2.1%
2 3
 
0.6%
3 1
 
0.2%
9 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316
66.7%
Other Punctuation 158
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
50.0%
7 143
45.3%
8 10
 
3.2%
2 3
 
0.9%
3 1
 
0.3%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 158
33.3%
0 158
33.3%
7 143
30.2%
8 10
 
2.1%
2 3
 
0.6%
3 1
 
0.2%
9 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 158
33.3%
0 158
33.3%
7 143
30.2%
8 10
 
2.1%
2 3
 
0.6%
3 1
 
0.2%
9 1
 
0.2%

DTRECEBIM
Real number (ℝ)

Distinct32
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17863340
Minimum1042022
Maximum31012022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:54.425894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1042022
5-th percentile2092022
Q111042022
median19567022
Q325042022
95-th percentile29042022
Maximum31012022
Range29970000
Interquartile range (IQR)14000000

Descriptive statistics

Standard deviation8738028.6
Coefficient of variation (CV)0.48915985
Kurtosis-1.1630269
Mean17863340
Median Absolute Deviation (MAD)6525000
Skewness-0.36795705
Sum4.6087417 × 109
Variance7.6353144 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:54.582144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8042022 28
 
10.9%
25042022 28
 
10.9%
29042022 26
 
10.1%
22032022 23
 
8.9%
13042022 18
 
7.0%
26082022 14
 
5.4%
18022022 12
 
4.7%
4032022 12
 
4.7%
25032022 11
 
4.3%
1042022 11
 
4.3%
Other values (22) 75
29.1%
ValueCountFrequency (%)
1042022 11
 
4.3%
2092022 3
 
1.2%
3052022 1
 
0.4%
4032022 12
4.7%
5082022 2
 
0.8%
7112022 3
 
1.2%
8032022 1
 
0.4%
8042022 28
10.9%
10062022 1
 
0.4%
11022022 1
 
0.4%
ValueCountFrequency (%)
31012022 2
 
0.8%
29072022 3
 
1.2%
29062022 1
 
0.4%
29042022 26
10.1%
27072022 6
 
2.3%
27052022 1
 
0.4%
26122022 5
 
1.9%
26082022 14
5.4%
25042022 28
10.9%
25032022 11
 
4.3%

ATESTADO
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct237
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
B342 U071 U049
 
5
J960/B342 U071
 
4
/B342 U071 U049
 
4
B342 U071
 
2
J960/B342 U071 U049
 
2
Other values (232)
241 

Length

Max length39
Median length33.5
Mean length23.662791
Min length9

Characters and Unicode

Total characters6105
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)86.4%

Sample

1st rowJ960/B342 U071*J690 G20
2nd rowR570/A419/A419/B342 U071*I694 I10
3rd rowB342 U071 U049/E119*G20
4th row/ /B342 U071 U049/E119
5th rowR578/A419/B342 U072

Common Values

ValueCountFrequency (%)
B342 U071 U049 5
 
1.9%
J960/B342 U071 4
 
1.6%
/B342 U071 U049 4
 
1.6%
B342 U071 2
 
0.8%
J960/B342 U071 U049 2
 
0.8%
A419/J984/B342 U071 2
 
0.8%
J960/B342 U071*G309 2
 
0.8%
B342 U071 U049*G20 2
 
0.8%
J984/B342 U071 2
 
0.8%
R092/R090/B342 U071 U049 2
 
0.8%
Other values (227) 231
89.5%

Length

2023-05-01T23:48:54.770058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u071 88
 
12.5%
b342 58
 
8.3%
u049 24
 
3.4%
i10 22
 
3.1%
j960/b342 19
 
2.7%
u072 19
 
2.7%
e149 16
 
2.3%
u071*i10 16
 
2.3%
a419/b342 14
 
2.0%
a419/j189/b342 12
 
1.7%
Other values (263) 414
59.0%

Most occurring characters

ValueCountFrequency (%)
1 612
 
10.0%
0 577
 
9.5%
9 560
 
9.2%
4 545
 
8.9%
/ 474
 
7.8%
444
 
7.3%
2 395
 
6.5%
3 330
 
5.4%
7 321
 
5.3%
U 317
 
5.2%
Other values (18) 1530
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3734
61.2%
Uppercase Letter 1287
 
21.1%
Other Punctuation 640
 
10.5%
Space Separator 444
 
7.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 317
24.6%
B 259
20.1%
J 219
17.0%
I 126
 
9.8%
A 108
 
8.4%
R 59
 
4.6%
E 49
 
3.8%
N 44
 
3.4%
G 30
 
2.3%
C 29
 
2.3%
Other values (5) 47
 
3.7%
Decimal Number
ValueCountFrequency (%)
1 612
16.4%
0 577
15.5%
9 560
15.0%
4 545
14.6%
2 395
10.6%
3 330
8.8%
7 321
8.6%
8 169
 
4.5%
6 130
 
3.5%
5 95
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 474
74.1%
* 166
 
25.9%
Space Separator
ValueCountFrequency (%)
444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4818
78.9%
Latin 1287
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 317
24.6%
B 259
20.1%
J 219
17.0%
I 126
 
9.8%
A 108
 
8.4%
R 59
 
4.6%
E 49
 
3.8%
N 44
 
3.4%
G 30
 
2.3%
C 29
 
2.3%
Other values (5) 47
 
3.7%
Common
ValueCountFrequency (%)
1 612
12.7%
0 577
12.0%
9 560
11.6%
4 545
11.3%
/ 474
9.8%
444
9.2%
2 395
8.2%
3 330
6.8%
7 321
6.7%
8 169
 
3.5%
Other values (3) 391
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 612
 
10.0%
0 577
 
9.5%
9 560
 
9.2%
4 545
 
8.9%
/ 474
 
7.8%
444
 
7.3%
2 395
 
6.5%
3 330
 
5.4%
7 321
 
5.3%
U 317
 
5.2%
Other values (18) 1530
25.1%

DTRECORIGA
Real number (ℝ)

Distinct33
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17120898
Minimum1042022
Maximum31012022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:54.940286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1042022
5-th percentile1042022
Q110302022
median18022022
Q325042022
95-th percentile29042022
Maximum31012022
Range29970000
Interquartile range (IQR)14740000

Descriptive statistics

Standard deviation8568849.5
Coefficient of variation (CV)0.50049066
Kurtosis-1.1145694
Mean17120898
Median Absolute Deviation (MAD)7020000
Skewness-0.33640181
Sum4.4171917 × 109
Variance7.3425182 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:55.107839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
8042022 29
 
11.2%
22032022 29
 
11.2%
25042022 22
 
8.5%
13042022 19
 
7.4%
18022022 16
 
6.2%
26082022 14
 
5.4%
1042022 14
 
5.4%
4032022 12
 
4.7%
25032022 12
 
4.7%
29042022 11
 
4.3%
Other values (23) 80
31.0%
ValueCountFrequency (%)
1042022 14
5.4%
2092022 3
 
1.2%
3052022 1
 
0.4%
4022022 1
 
0.4%
4032022 12
4.7%
5082022 2
 
0.8%
7112022 2
 
0.8%
8042022 29
11.2%
10062022 1
 
0.4%
11022022 1
 
0.4%
ValueCountFrequency (%)
31012022 4
 
1.6%
29072022 3
 
1.2%
29062022 1
 
0.4%
29042022 11
4.3%
27072022 6
 
2.3%
27052022 1
 
0.4%
26122022 5
 
1.9%
26082022 14
5.4%
25042022 22
8.5%
25032022 12
4.7%

OPOR_DO
Real number (ℝ)

Distinct76
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.011628
Minimum5
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:55.293916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17
Q133
median50
Q363
95-th percentile79
Maximum114
Range109
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.803015
Coefficient of variation (CV)0.41246289
Kurtosis-0.53121859
Mean48.011628
Median Absolute Deviation (MAD)15
Skewness-0.010302473
Sum12387
Variance392.1594
MonotonicityNot monotonic
2023-05-01T23:48:55.481808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 11
 
4.3%
60 9
 
3.5%
24 7
 
2.7%
50 7
 
2.7%
58 7
 
2.7%
57 7
 
2.7%
56 7
 
2.7%
20 7
 
2.7%
64 6
 
2.3%
54 6
 
2.3%
Other values (66) 184
71.3%
ValueCountFrequency (%)
5 1
 
0.4%
7 1
 
0.4%
8 1
 
0.4%
11 2
 
0.8%
12 2
 
0.8%
13 2
 
0.8%
14 1
 
0.4%
16 1
 
0.4%
17 4
1.6%
18 5
1.9%
ValueCountFrequency (%)
114 1
 
0.4%
91 1
 
0.4%
87 1
 
0.4%
84 1
 
0.4%
83 1
 
0.4%
82 1
 
0.4%
81 1
 
0.4%
80 5
1.9%
79 2
 
0.8%
78 4
1.6%

CAUSAMAT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

ESCMAEAGR1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

ESCFALAGR1
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)4.1%
Missing13
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean8.3183673
Minimum0
Maximum12
Zeros45
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:55.633735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median10
Q311
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.2672828
Coefficient of variation (CV)0.51299523
Kurtosis-0.0664136
Mean8.3183673
Median Absolute Deviation (MAD)2
Skewness-1.2301516
Sum2038
Variance18.209702
MonotonicityNot monotonic
2023-05-01T23:48:55.779749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
12 58
22.5%
10 56
21.7%
0 45
17.4%
11 30
11.6%
9 28
10.9%
8 23
 
8.9%
2 2
 
0.8%
4 1
 
0.4%
1 1
 
0.4%
7 1
 
0.4%
(Missing) 13
 
5.0%
ValueCountFrequency (%)
0 45
17.4%
1 1
 
0.4%
2 2
 
0.8%
4 1
 
0.4%
7 1
 
0.4%
8 23
 
8.9%
9 28
10.9%
10 56
21.7%
11 30
11.6%
12 58
22.5%
ValueCountFrequency (%)
12 58
22.5%
11 30
11.6%
10 56
21.7%
9 28
10.9%
8 23
 
8.9%
7 1
 
0.4%
4 1
 
0.4%
2 2
 
0.8%
1 1
 
0.4%
0 45
17.4%

STDOEPIDEM
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0.0
258 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters774
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 258
100.0%

Length

2023-05-01T23:48:55.925168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:56.063999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 258
100.0%

Most occurring characters

ValueCountFrequency (%)
0 516
66.7%
. 258
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 516
66.7%
Other Punctuation 258
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 516
100.0%
Other Punctuation
ValueCountFrequency (%)
. 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 516
66.7%
. 258
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 516
66.7%
. 258
33.3%

STDONOVA
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
257 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters258
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

Length

2023-05-01T23:48:56.178591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:56.332994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 257
99.6%
0 1
 
0.4%

DIFDATA
Real number (ℝ)

Distinct88
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.178295
Minimum7
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:56.478405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile18.85
Q136.25
median53
Q366
95-th percentile110.45
Maximum228
Range221
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation27.576409
Coefficient of variation (CV)0.49976915
Kurtosis6.034895
Mean55.178295
Median Absolute Deviation (MAD)15
Skewness1.5283347
Sum14236
Variance760.45836
MonotonicityNot monotonic
2023-05-01T23:48:56.661692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 9
 
3.5%
66 9
 
3.5%
56 7
 
2.7%
57 6
 
2.3%
44 6
 
2.3%
39 6
 
2.3%
53 6
 
2.3%
24 6
 
2.3%
50 6
 
2.3%
58 6
 
2.3%
Other values (78) 191
74.0%
ValueCountFrequency (%)
7 1
 
0.4%
11 2
0.8%
12 2
0.8%
13 2
0.8%
14 1
 
0.4%
16 1
 
0.4%
17 1
 
0.4%
18 3
1.2%
19 3
1.2%
20 4
1.6%
ValueCountFrequency (%)
228 1
 
0.4%
161 1
 
0.4%
131 1
 
0.4%
118 3
1.2%
116 2
0.8%
115 1
 
0.4%
114 2
0.8%
113 2
0.8%
110 2
0.8%
107 1
 
0.4%

NUDIASOBCO
Real number (ℝ)

Distinct6
Distinct (%)85.7%
Missing251
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean38.285714
Minimum9
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:56.846475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.3
Q120
median29
Q346.5
95-th percentile86.8
Maximum97
Range88
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation30.928489
Coefficient of variation (CV)0.80783367
Kurtosis1.3466817
Mean38.285714
Median Absolute Deviation (MAD)9
Skewness1.4100831
Sum268
Variance956.57143
MonotonicityNot monotonic
2023-05-01T23:48:57.020067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20 2
 
0.8%
29 1
 
0.4%
9 1
 
0.4%
97 1
 
0.4%
63 1
 
0.4%
30 1
 
0.4%
(Missing) 251
97.3%
ValueCountFrequency (%)
9 1
0.4%
20 2
0.8%
29 1
0.4%
30 1
0.4%
63 1
0.4%
97 1
0.4%
ValueCountFrequency (%)
97 1
0.4%
63 1
0.4%
30 1
0.4%
29 1
0.4%
20 2
0.8%
9 1
0.4%

DTCADINV
Categorical

Distinct5
Distinct (%)71.4%
Missing251
Missing (%)97.3%
Memory size4.0 KiB
21022022.0
3 
28042022.0
1 
4052022.0
1 
1082022.0
1 
7022022.0
1 

Length

Max length10
Median length10
Mean length9.5714286
Min length9

Characters and Unicode

Total characters67
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)57.1%

Sample

1st row28042022.0
2nd row21022022.0
3rd row4052022.0
4th row21022022.0
5th row21022022.0

Common Values

ValueCountFrequency (%)
21022022.0 3
 
1.2%
28042022.0 1
 
0.4%
4052022.0 1
 
0.4%
1082022.0 1
 
0.4%
7022022.0 1
 
0.4%
(Missing) 251
97.3%

Length

2023-05-01T23:48:57.222214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:57.457747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
21022022.0 3
42.9%
28042022.0 1
 
14.3%
4052022.0 1
 
14.3%
1082022.0 1
 
14.3%
7022022.0 1
 
14.3%

Most occurring characters

ValueCountFrequency (%)
2 29
43.3%
0 21
31.3%
. 7
 
10.4%
1 4
 
6.0%
8 2
 
3.0%
4 2
 
3.0%
5 1
 
1.5%
7 1
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
89.6%
Other Punctuation 7
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29
48.3%
0 21
35.0%
1 4
 
6.7%
8 2
 
3.3%
4 2
 
3.3%
5 1
 
1.7%
7 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29
43.3%
0 21
31.3%
. 7
 
10.4%
1 4
 
6.0%
8 2
 
3.0%
4 2
 
3.0%
5 1
 
1.5%
7 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29
43.3%
0 21
31.3%
. 7
 
10.4%
1 4
 
6.0%
8 2
 
3.0%
4 2
 
3.0%
5 1
 
1.5%
7 1
 
1.5%

TPOBITOCOR
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing251
Missing (%)97.3%
Memory size4.0 KiB
9.0
7 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 7
 
2.7%
(Missing) 251
97.3%

Length

2023-05-01T23:48:57.675768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:57.835374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.0 7
100.0%

Most occurring characters

ValueCountFrequency (%)
9 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
66.7%
Other Punctuation 7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 7
50.0%
0 7
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 7
33.3%
. 7
33.3%
0 7
33.3%

DTCONINV
Real number (ℝ)

Distinct6
Distinct (%)85.7%
Missing251
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean10323451
Minimum1082022
Maximum21022022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:57.944815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1082022
5-th percentile1664022
Q13522022
median4052022
Q319532022
95-th percentile21022022
Maximum21022022
Range19940000
Interquartile range (IQR)16010000

Descriptive statistics

Standard deviation9185576.4
Coefficient of variation (CV)0.88977773
Kurtosis-2.5805878
Mean10323451
Median Absolute Deviation (MAD)2970000
Skewness0.37138275
Sum72264154
Variance8.4374814 × 1013
MonotonicityNot monotonic
2023-05-01T23:48:58.074287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
21022022 2
 
0.8%
18042022 1
 
0.4%
3022022 1
 
0.4%
4052022 1
 
0.4%
1082022 1
 
0.4%
4022022 1
 
0.4%
(Missing) 251
97.3%
ValueCountFrequency (%)
1082022 1
0.4%
3022022 1
0.4%
4022022 1
0.4%
4052022 1
0.4%
18042022 1
0.4%
21022022 2
0.8%
ValueCountFrequency (%)
21022022 2
0.8%
18042022 1
0.4%
4052022 1
0.4%
4022022 1
0.4%
3022022 1
0.4%
1082022 1
0.4%

TPRESGINFO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size4.0 KiB

TPNIVELINV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing251
Missing (%)97.3%
Memory size4.0 KiB
M
7 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 7
 
2.7%
(Missing) 251
97.3%

Length

2023-05-01T23:48:58.229004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:58.378522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 7
100.0%

Most occurring characters

ValueCountFrequency (%)
M 7
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 7
100.0%

CAUSABAS_O
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
B342
254 
E119
 
2
V049
 
1
J988
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1032
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowB342
2nd rowB342
3rd rowE119
4th rowE119
5th rowB342

Common Values

ValueCountFrequency (%)
B342 254
98.4%
E119 2
 
0.8%
V049 1
 
0.4%
J988 1
 
0.4%

Length

2023-05-01T23:48:58.497945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:58.656616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
b342 254
98.4%
e119 2
 
0.8%
v049 1
 
0.4%
j988 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
4 255
24.7%
B 254
24.6%
3 254
24.6%
2 254
24.6%
1 4
 
0.4%
9 4
 
0.4%
E 2
 
0.2%
8 2
 
0.2%
V 1
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 774
75.0%
Uppercase Letter 258
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 255
32.9%
3 254
32.8%
2 254
32.8%
1 4
 
0.5%
9 4
 
0.5%
8 2
 
0.3%
0 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 254
98.4%
E 2
 
0.8%
V 1
 
0.4%
J 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 774
75.0%
Latin 258
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 255
32.9%
3 254
32.8%
2 254
32.8%
1 4
 
0.5%
9 4
 
0.5%
8 2
 
0.3%
0 1
 
0.1%
Latin
ValueCountFrequency (%)
B 254
98.4%
E 2
 
0.8%
V 1
 
0.4%
J 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 255
24.7%
B 254
24.6%
3 254
24.6%
2 254
24.6%
1 4
 
0.4%
9 4
 
0.4%
E 2
 
0.2%
8 2
 
0.2%
V 1
 
0.1%
0 1
 
0.1%

TPPOS
Categorical

Distinct2
Distinct (%)0.8%
Missing9
Missing (%)3.5%
Memory size4.0 KiB
S
160 
N
89 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowS
4th rowS
5th rowN

Common Values

ValueCountFrequency (%)
S 160
62.0%
N 89
34.5%
(Missing) 9
 
3.5%

Length

2023-05-01T23:48:58.799330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T23:48:58.939777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 160
64.3%
n 89
35.7%

Most occurring characters

ValueCountFrequency (%)
S 160
64.3%
N 89
35.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 249
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 160
64.3%
N 89
35.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 249
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 160
64.3%
N 89
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 160
64.3%
N 89
35.7%

IDADE2
Real number (ℝ)

Distinct58
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.27907
Minimum22
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-05-01T23:48:59.082271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile48.55
Q169
median79
Q386
95-th percentile94
Maximum99
Range77
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.182785
Coefficient of variation (CV)0.18593285
Kurtosis1.5003473
Mean76.27907
Median Absolute Deviation (MAD)8
Skewness-1.1098127
Sum19680
Variance201.15139
MonotonicityNot monotonic
2023-05-01T23:48:59.272234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 11
 
4.3%
85 11
 
4.3%
87 11
 
4.3%
86 9
 
3.5%
79 9
 
3.5%
82 9
 
3.5%
74 8
 
3.1%
83 8
 
3.1%
90 8
 
3.1%
77 7
 
2.7%
Other values (48) 167
64.7%
ValueCountFrequency (%)
22 1
0.4%
26 1
0.4%
27 1
0.4%
32 1
0.4%
34 1
0.4%
41 2
0.8%
43 1
0.4%
44 2
0.8%
45 2
0.8%
46 1
0.4%
ValueCountFrequency (%)
99 1
 
0.4%
98 4
1.6%
96 1
 
0.4%
95 5
1.9%
94 7
2.7%
93 3
 
1.2%
92 6
2.3%
91 6
2.3%
90 8
3.1%
89 7
2.7%

Interactions

2023-05-01T23:48:32.533531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:46:59.707422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-01T23:47:24.899862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-01T23:48:27.189759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:31.247141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:35.516734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:02.882528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:06.920875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:11.386089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:16.140543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:19.552620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:23.743639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.142110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:32.148070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.058487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:39.935241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:44.776674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:48.800194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:52.507097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:57.468673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.070555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:05.297716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-01T23:47:16.317448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:19.994858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:23.943731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.277828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:32.335120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.184307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:40.116488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:44.952050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:48.931953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:52.687169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:57.592288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.263471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:05.444154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:10.022472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:14.083327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:19.077773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:23.032922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:27.574102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:31.611643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:35.952449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:03.275570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:07.201384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:11.826726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:16.461546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:20.136306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:24.142823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.426998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:32.526826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.321800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:40.788127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:45.139852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:49.073941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:52.876127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:57.730532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.456235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:05.626541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:10.239300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:14.275353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:19.265517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:23.227024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:27.767700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:31.798391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:36.143265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:03.404342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:07.325394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:12.036746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:16.596606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:20.279292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:24.326039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.561760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:32.702047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.444904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:40.988421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:45.301812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:49.245390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:53.049301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:57.881845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.641597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:05.805745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:10.429619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:15.150775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:19.440144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:23.403131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:27.947170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:31.988945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:36.312711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:03.528451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:07.465950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:12.476304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:16.720000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:20.414328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:24.491064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.688038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:32.866418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.576573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:41.190775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:45.469017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:49.412666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:53.238594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:58.014980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.799937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:05.981214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:10.624629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:15.321075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:19.608843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:23.590951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:28.113739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:32.167545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:36.499961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:03.677469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:07.643592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:12.658636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:16.863234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:20.566500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:24.682753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:28.827014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:33.067363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:36.726967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:41.403345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:45.658370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:49.602503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:53.442621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:47:58.179834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:01.999650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:06.195316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:10.840369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:15.520643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:19.804164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:23.799614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:28.298492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T23:48:32.362626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-05-01T23:48:37.950714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-01T23:48:38.609402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-01T23:48:39.471273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ORIGEMTIPOBITODTOBITOHORAOBITONATURALCODMUNNATUDTNASCIDADESEXORACACORESTCIVESCESC2010SERIESCFALOCUPCODMUNRESLOCOCORCODESTABCODMUNOCORIDADEMAEESCMAEESCMAE2010SERIESCMAEOCUPMAEQTDFILVIVOQTDFILMORTGRAVIDEZSEMAGESTACGESTACAOPARTOOBITOPARTOPESOTPMORTEOCOOBITOGRAVOBITOPUERPASSISTMEDEXAMECIRURGIANECROPSIALINHAALINHABLINHACLINHADLINHAIICAUSABASCB_PRECOMUNSVOIMDTATESTADOCIRCOBITOACIDTRABFONTENUMEROLOTEDTINVESTIGDTCADASTROATESTANTESTCODIFICACODIFICADOVERSAOSISTVERSAOSCBFONTEINVDTRECEBIMATESTADODTRECORIGAOPOR_DOCAUSAMATESCMAEAGR1ESCFALAGR1STDOEPIDEMSTDONOVADIFDATANUDIASOBCODTCADINVTPOBITOCORDTCONINVTPRESGINFOTPNIVELINVCAUSABAS_OTPPOSIDADE2
99481225012022700.0824.0240810.010021937.0484.011.02.04.03.0NaNNaN24081012656930.0240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN*J960*B342*U071NaNNaN*J690*G20XB342NaNNaN25012022.0NaNNaNNaN20220009.0NaN23022022.02.0SS3.2.303.4NaN4032022.0J960/B342 U071*J690 G20403202238NaNNaN12.00.0138NaNNaNNaNNaNNaNNaNB342N84.0
106881222012022805.0824.0241250.015091934.0487.021.03.02.01.0NaN333115.024081012656930.0240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaN2.0*R570*A419*A419*B342*U071*I694*I10XB342NaNNaN22012022.0NaNNaNNaN20220009.0NaN24022022.02.0SS3.2.303.4NaN4032022.0R570/A419/A419/B342 U071*I694 I10403202241NaNNaN10.00.0141NaNNaNNaNNaNNaNNaNB342N87.0
1069012260120222030.0824.0240810.09051958.0463.011.02.04.03.0NaN999993.02408103NaN240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaN2.0*B342*U071*U049*E119NaNNaN*G20XB342NaN240810.027012022.0NaNNaNNaN20220020.010022022.024022022.04.0SS3.2.303.48.025042022.0B342 U071 U049/E119*G20403202237NaNNaN12.00.0189NaNNaNNaNNaNNaNNaNE119S63.0
1069312260120221507.0824.0240030.020081956.0465.011.01.03.01.04.0262705.02408103NaN240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaN2.0NaNNaN*B342*U071*U049*E119NaNB342NaN240810.027012022.0NaNNaNNaN20220020.010022022.024022022.04.0SS3.2.303.48.025042022.0/ /B342 U071 U049/E119403202237NaNNaN2.00.0189NaNNaNNaNNaNNaNNaNE119S65.0
1069712190120222025.0826.0261160.026111958.0463.024.02.03.02.0NaN999992.024081012656930.0240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.02.03.01.0NaNNaN2.0*R578*A419*B342*U072NaNNaNB342NaNNaN19012022.0NaNNaNNaN20220009.0NaN24022022.0NaNSS3.2.303.4NaN4032022.0R578/A419/B342 U072403202244NaNNaN11.00.0144NaNNaNNaNNaNNaNNaNB342N63.0
126311220022022900.0824.0240810.022021947.0474.014.01.01.00.0NaN354705.024081013708926.0240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN*B342*U071*U049NaNNaN*G20XB342NaNNaN20022022.0NaNNaNNaN20220017.020022022.06042022.01.0SS3.2.303.47.08042022.0/B342 U071 U049*G20804202247NaNNaN0.00.0147NaNNaNNaNNaNNaNNaNB342S74.0
126321215022022525.0824.0240810.01031934.0487.022.03.03.02.0NaN763015.024081013708926.0240810NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaN2.0*R579*A419*J189*B342*U071*N039*I509B342NaNNaN15022022.0NaNNaNNaN20220017.015022022.06042022.01.0SS3.2.303.47.08042022.0R579/A419/J189/B342 U071*N039 I509804202252NaNNaN11.00.0152NaNNaNNaNNaNNaNNaNB342S87.0
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